Perceived Alzheimer\u27s Disease Threat as a Predictor of Behavior Change to Lower Disease Risk: The Gray Matters Study

Alzheimer’s disease is a growing public health concern with the current number afflicted of 5 million in the US expected to triple by 2050. Since there is currently no cure or preventive pharmacological treatment, AD prevention research is now recognized as an important enterprise, with a goal to identify modifiable lifestyle factors that can reduce AD risk or delay its onset. Among these, increased physical activity, healthier food choices, more cognitive stimulation, better sleep quality, stress management, and social engagement have been identified as reasonable targets for behavioral intervention. A smartphone application-based behavioral intervention targeting these six behavioral domains was recently developed and a six-month randomized controlled trial was conducted, both to determine feasibility and compliance with technology usage and to test its efficacy. This study, titled the Gray Matters Study, was conducted in Cache County, Utah, enrolling a sample of 146 middle-aged participants (aged 40 to 64 years) randomized to treatment or control condition. Under the Health Belief Model, individuals who perceive a greater susceptibility to a particular health condition are hypothesized to be more likely to engage in more positive behaviors to reduce disease risk. Following this model, perceived threat of AD (operationalized by fear of AD, family history of AD, and metacognitive concerns) was examined for prediction of behavioral change over the six-month Gray Matters intervention period in these same six behavioral domains. Persons with a moderate level of fear of AD made significantly greater improvements in physical activity than those with low or high levels of fear. Family history was not a significant predictor of health-related behavioral change. However, persons with a moderate level of metacognitive concerns made significantly greater improvements in both physical activity and food quality than those with low or high levels of concerns. This is the first study to examine these psychological constructs related to AD risk and the extent to which they predict health-related behavior change. Future studies should extend the length of follow-up to at least one full year, include a more diverse sample of participants to expand generalizability, and build upon these findings to personalize supportive behavioral change interventions in order to be sensitive to these psychological factors

Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients.

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition;emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; andrecognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Background: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. Objective: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. Methods: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. Results: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. Conclusions: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant

Enhancing behavioural changes: a narrative review on the effectiveness of a multifactorial APP-based intervention integrating physical activity

The rapid evolution of technologies is a key innovation in the organisation and management of physical activities (PA) and sports. The increase in benefits and opportunities related to the adoption of technologies for both the promotion of a healthy lifestyle and the management of chronic diseases is evident. In the field of telehealth, these devices provide personalised recommendations, workout monitoring and injury prevention. The study aimed to provide an overview of the landscape of technology application to PA organised to promote active lifestyles and improve chronic disease management. This review identified specific areas of focus for the selection of articles: the utilisation of mobile APPs and technological devices for enhancing weight loss, improving cardiovascular health, managing diabetes and cancer and preventing osteoporosis and cognitive decline. A multifactorial intervention delivered via mobile APPs, which integrates PA while managing diet or promoting social interaction, is unquestionably more effective than a singular intervention. The main finding related to promoting PA and a healthy lifestyle through app usage is associated with "behaviour change techniques". Even when individuals stop using the APP, they often maintain the structured or suggested lifestyle habits initially provided by the APP. Various concerns regarding the excessive use of APPs need to be addressed

The Gray Matter project: modificating lifestyles to prevent dementia

As dementia aetiology is based on different bio-psychosocial factors, prevention strategies for dementia have recently focused on multi-domain interventions of individuals at risk and/or with a normative cognitive level, encouraging the lifestyle change through combined programs of physical activity, cognitive training, nutrition education and social activities (in chapter 1, a narrative review of these studies is presented). Most of the multidomain intervention aimed on the prevention of cognitive disease are carried on with elderly patients with a mild cognitive decline or on at-risk adult categories. 5 Caregivers of patients with dementia are considered as an at-risk category. The majority of them (86%) are represented by family members (prominently women) who are also defined as “informal caregivers”. They fulfil their caringgiving role from 7 to 11 h a day on average, up to 10-15 h when clinical conditions worsen 10. Informal caregivers have to cope with physical, psychological and social stressors that affect their health conditions and quality of life negatively (Eleuteri et al., 2016). The burdens of caregiving include many things that have been shown to increase the risk of cognitive decline, including chronic stress, social isolation, depression, decreased physical activity, and a shift in eating habits toward more fast food and significantly more weight compared with controls (Vitaliano et al., 1996). This could be connected with the important role that sleep plays between stress and metabolic health (Geiker et al., 2018). Being a caregiver has been found to be a factor affecting negatively sleep quality (Brummett et al., 2006). Interventions to promote positive lifestyles are, therefore, important in order to improve the caregivers’ general health and, specifically, to prevent the cognitive decline. In the second chapter, an article recently published specifies the importance of multimodal interventions in ameliorating caregivers’ health, since complex moderation and mediation effects exist between the different areas involved in the AD risk reduction. The third chapter will, finally, describe the results of the Gray Matter Project, a multidomain pilot RCT, firstly carried out done in Cache County, Utah designed to promote positive changes in lifestyle (exercise, nutrition, cognitive stimulation, social engagement, stress management, and sleep quality), specifically for the purpose of reducing AD risk in family caregivers of elderly with dementia

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions